Scales, Axes & Coordinate systems

Scales

Within ggplot there are a bunch of scales that control how a plot maps data values to the visual values of an aesthetic. To change the mapping, add a custom scale. These scales look something like (the ggplot2-cheatsheet is helpful as well):

• scale_x_continuous; change your x-axis when your x-variable is continuous (scale_y_continuous for y-axis)

• scale_x_discrete; change your x-axis when your x-variable is categorical (scale_y_discrete for y-axis)

• scale_*_identity; use data values as visual values (e.g., scale_colour_identity when your variable in colour = variable contains the names of colours). * can be fill, colour, shape, size

• scale_*_manual(values = c()); map discrete values to manually chosen visual values (e.g., scale_fill_manual(values = c("4" = "blue", "r" = "red", "f" = "black")) * can be fill, colour, shape, size.

scale_x_continuous

Let’s see what scale_x_continuous and scale_y_continuous can do.

We’ll change features from this graph

ggplot(mpg, aes(x = hwy, y = cty)) +
  geom_point()

Breaks

ggplot(mpg, aes(x = hwy, y = cty)) +
  geom_point() +
  scale_x_continuous(breaks = c(15, 20, 25, 30, 35, 40, 45))

# this would do the same scale_x_continuous(breaks = seq(15, 45, by = 5))

ggplot(mpg, aes(x = hwy, y = cty)) +
  geom_point() +
  scale_x_continuous(breaks = c(15, 20, 25, 30, 35, 40, 45)) +
  scale_y_continuous(breaks = c(9, 26, 35.5))

Zooming in and out with limits

For instance, often we want to zoom in or out. Let’s zoom in on the y-axis and zoom out on the x-axis.

ggplot(mpg, aes(x = hwy, y = cty)) +
  geom_point() +
  scale_x_continuous(limits = c(0, 75)) +
  scale_y_continuous(limits = c(20, 30))

## Warning: Removed 180 rows containing missing values or values outside the scale range
## (`geom_point()`).

It works! Importantly, though, we must realise that there are many datapoints not visualized currently, as the warning message in R already suggests.

Using the limits in this way can be a bit dangerous. Let’s see why in an example with mean + error plots:

ggplot(mpg, aes(x = manufacturer, y = cty)) +
  geom_bar(stat = "summary", fun = "mean") +
  geom_errorbar(stat = "summary", fun.data = "mean_se") +
  geom_point(colour = "lightblue")

Now let’s see what happens if we give limits that exclude some points

ggplot(mpg, aes(x = manufacturer, y = cty)) +
  geom_bar(stat = "summary", fun = "mean") +
  geom_errorbar(stat = "summary", fun.data = "mean_se") +
  geom_point(colour = "lightblue") +
  scale_y_continuous(limits = c(0, 25))

## Warning: Removed 8 rows containing non-finite outside the scale range
## (`stat_summary()`).
## Removed 8 rows containing non-finite outside the scale range
## (`stat_summary()`).

## Warning: Removed 8 rows containing missing values or values outside the scale range
## (`geom_point()`).

This is certainly not what we had in mind! We have changed our underlying data; all 8 datapoints that were larger than 25 were removed and then the data were plotted. Compare the bar and error for Honda and Volkswagen in the two graphs.

An alternative and preferred way of doing this, is doing it in the following way (also see coord_cartesian below):

ggplot(mpg, aes(x = manufacturer, y = cty)) +
  geom_bar(stat = "summary", fun = "mean") +
  geom_errorbar(stat = "summary", fun.data = "mean_se") +
  geom_point(colour = "lightblue") +
  coord_cartesian(ylim = c(0, 25))

Expand

ggplot adds some space to the bottom and the side of your axes. If you do not want this, use the expand() function. Compare the following two graphs:

ggplot(mpg, aes(x = displ, y = cty)) +
  geom_point() +
  scale_x_continuous(limits = c(0, 8)) +
  scale_y_continuous(limits = c(0, 36))

ggplot(mpg, aes(x = displ, y = cty)) +
  geom_point() +
  scale_x_continuous(limits = c(0, 8), expand = c(0, 0)) +
  scale_y_continuous(limits = c(0, 36), expand = c(0, 0))

But do note that you are risking throwing away datapoints again if you specify limits that exlude particular datapoints.

Transformations

There are also functions that allow you to transform the scales of the axes. For instance, you can think of having a logarithmic scale, or a square-root-transformed scale.

ggplot(mpg, aes(x = displ, y = cty)) +
  geom_point() +
  scale_x_continuous(trans = "sqrt")

Look closely at the x-axis. We have transformed the x-axis. This works much better for variables that have more skewed distributions.

Another example:

ggplot(mpg, aes(x = displ, y = cty)) +
  geom_point() +
  scale_y_continuous(trans = "reverse")

What happened?

Another example:

ggplot(mpg, aes(x = displ, y = cty)) +
  geom_point() +
  scale_y_continuous(trans = "log2")

What happened?

Labels

ggplot can also do something nifty with the labels of the axes. Let’s look at four examples to get some impressions (after we install a necessary package):

install.packages("scales")

library(scales)

ggplot(mpg, aes(x = displ, y = cty)) +
  geom_point() +
  scale_x_continuous(labels = scales::percent)

Cool that we can do this but obviously doesn’t make much sense.

ggplot(mpg, aes(x = displ, y = cty)) +
  geom_point() +
  scale_x_continuous(labels = scales::dollar)

Also nice that we can do this, but obviously not appropriate.

ggplot(mpg, aes(x = displ, y = cty)) +
  geom_point() +
  scale_y_continuous(labels = scientific)

ggplot(mpg, aes(x = displ, y = cty)) +
  geom_point() +
  scale_y_continuous(
    trans = "log2",
    breaks = trans_breaks("log2", function(x) 2^x),
    labels = trans_format("log2", math_format(2^.x))
  )

scale_x_discrete

When your x-axis is discrete (or categorical), you can use scale_x_discrete to change all sorts of relevant stuff. Let’s see how that works for the following graph:

ggplot(mpg, aes(x = drv, y = cty)) +
  geom_boxplot()

limits

ggplot(mpg, aes(x = drv, y = cty)) +
  geom_boxplot() +
  scale_x_discrete(limits = c("f", "r", "4"))

labels

ggplot(mpg, aes(x = drv, y = cty)) +
  geom_boxplot() +
  scale_x_discrete(labels = c("four", "front", "rear"))

This is a bit less error prone:

ggplot(mpg, aes(x = drv, y = cty)) +
  geom_boxplot() +
  scale_x_discrete(labels = c("4" = "four", "f" = "front", "r" = "rear"))

scale_colour_identity

Identity uses that values that are specified within a variable. Let’s create a column with colour names for the different types of drv.

library(tidyverse)
mpg <- mpg %>%
  mutate(colour = case_when(
    drv == "f" ~ "blue",
    drv == "r" ~ "orange",
    drv == "4" ~ "green"
  ))

We can now use this variable:

ggplot(mpg, aes(x = drv, y = cty, colour = colour)) +
  geom_boxplot() +
  scale_colour_identity()

Fill works too:

ggplot(mpg, aes(x = drv, y = cty, fill = colour)) +
  geom_boxplot() +
  scale_fill_identity()

scale_fill_manual

We could have created the above graph also with scale_fill_manual:

ggplot(mpg, aes(x = drv, y = cty, fill = colour)) +
  geom_boxplot() +
  scale_fill_manual(values = c("blue", "green", "orange"))

Again, better to be a bit specific, to be more sure that the colour is added to the right group:

ggplot(mpg, aes(x = drv, y = cty, fill = drv)) +
  geom_boxplot() +
  scale_fill_manual(values = c("f" = "blue", "4" = "green", "r" = "orange"))

Let’s try to control the order and labels of the legend!

ggplot(mpg, aes(x = drv, y = cty, fill = drv)) +
  geom_boxplot() +
  scale_fill_manual(
    values = c("f" = "blue", "4" = "green", "r" = "orange"),
    breaks = c("f", "r", "4"),
    labels = c("4" = "four", "f" = "front", "r" = "rear")
  )

scale_size_manual

Let’s do another example:

ggplot(mpg, aes(x = drv, y = cty)) +
  geom_count()

Let’s change the breaks on the legend.

ggplot(mpg, aes(x = drv, y = cty)) +
  geom_count() +
  scale_size_continuous(breaks = c(1, 5, 10))

Coordinate systems

ggplot has several coordinate systems, you’ll mostly use the default coord_cartesian, but also coord_fixed and coord_flip might come in handy.

coord_cartesian

ggplot(mpg, aes(x = hwy, y = cty)) +
  geom_count()

Through coord_caresian we can zoom:

ggplot(mpg, aes(x = hwy, y = cty)) +
  geom_count() +
  coord_cartesian(xlim = c(20, 40), ylim = c(11, 26))

coord_fixed

coord_fixed forces a specified ratio between the physical representation of data units on the axes. This is very useful when the variables on the x-axis and on the y-axis have the same units:

ggplot(mpg, aes(x = hwy, y = cty)) +
  geom_count() +
  coord_fixed()

coord_flip

coord_flip reverses the x- and y-axis. This is often useful with barcharts:

ggplot(mpg, aes(x = reorder(manufacturer, cty, length))) +
  geom_bar()

ggplot(mpg, aes(x = reorder(manufacturer, cty, length))) +
  geom_bar() +
  coord_flip()